{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import tensorflow_compression as tfc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "import h5py\n",
    "\n",
    "from BalleFFP_improved import BalleFFP\n",
    "from BalleHP  import BalleHP\n",
    "from read_data import read_data_numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"font.family\"] = \"serif\"\n",
    "plt.rcParams[\"font.size\"]   = 14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ffp_channels_last_epochs5_normFalse_l0.5_20230306172822.h5\n"
     ]
    }
   ],
   "source": [
    "MODEL_FOLDER = \"../models/\"\n",
    "MODEL = \"ffp\"\n",
    "CH_FORMAT = \"channels_last\"\n",
    "EPOCHS = 5\n",
    "NORM = False\n",
    "DATIME = None\n",
    "\n",
    "if DATIME is None:\n",
    "    files = []\n",
    "    for file in next(os.walk(MODEL_FOLDER))[2]:\n",
    "        if f'model_{MODEL}_{CH_FORMAT}_epochs{EPOCHS}_norm{NORM}' in file:\n",
    "            files.append(file[6:])\n",
    "    \n",
    "    SELECTED_MODEL = files[0]\n",
    "else:\n",
    "    SELECTED_MODEL = f'model_{MODEL}_{CH_FORMAT}_epochs{EPOCHS}_norm{NORM}_{DATIME}.h5'\n",
    "    \n",
    "print(SELECTED_MODEL)\n",
    "MODEL_NAME   = \"model_\" + SELECTED_MODEL\n",
    "LOSS_NAME    = \"losses_\" + SELECTED_MODEL\n",
    "\n",
    "DATA_FOLDER  = \"../data/stl10/stl10_binary/\"\n",
    "DATA_FILE    = \"unlabeled_X.bin\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path = os.path.join(MODEL_FOLDER, MODEL_NAME)\n",
    "loss_path  = os.path.join(MODEL_FOLDER, LOSS_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_path_2 = '../vae/vae_image_compression_weights'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open the .h5 file containing the losses and load them as two numpy arrays train_loss and test_loss\n",
    "with h5py.File(loss_path, 'r') as f: # type: ignore\n",
    "    train_loss = np.array(f['train'])\n",
    "    test_loss  = np.array(f['test'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cProfile import label\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 5), constrained_layout=True)\n",
    "ax.grid(True, which='both', ls='-', lw=0.5, c='grey', alpha=0.3)\n",
    "\n",
    "ax.plot(train_loss, lw=2, label=f\"train loss (final: {train_loss[-1]:.2f})\")\n",
    "ax.plot(test_loss,  lw=2, label=f\"test loss (final: {test_loss[-1]:.2f})\")\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.set_ylabel('Loss')\n",
    "\n",
    "fig.savefig(\"./plots/losses.png\", dpi=300, facecolor=\"w\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-06 17:30:17.720547: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-06 17:30:17.722974: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-06 17:30:17.734785: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-06 17:30:17.736744: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-06 17:30:17.738697: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-06 17:30:17.740584: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n"
     ]
    }
   ],
   "source": [
    "tf.config.set_visible_devices([], 'GPU')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-06 17:30:20.041990: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.checkpoint.checkpoint.CheckpointLoadStatus at 0x7fedb61931c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vae = BalleFFP(N=128, M=192, k2=3, c=3, format=CH_FORMAT)\n",
    "vae_2 = BalleHP(N=128, M=192, k1=3, k2=3, c=3, format=CH_FORMAT)\n",
    "\n",
    "_ = vae(tf.zeros((1, 96, 96, 3)))\n",
    "_ = vae_2(tf.zeros((1, 96, 96, 3)))\n",
    "\n",
    "# load the weights from the .h5 file\n",
    "vae.load_weights(model_path)\n",
    "vae_2.load_weights(model_path_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_path = os.path.join(DATA_FOLDER, DATA_FILE)\n",
    "data = read_data_numpy(data_path, CH_FORMAT).astype('float32') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def select_random_images(data, n):\n",
    "    \"\"\"Selects n random images from the data.\"\"\"\n",
    "    idx = np.random.choice(data.shape[0], n, replace=False)\n",
    "    return data[idx]\n",
    "\n",
    "img = select_random_images(data, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def visualize_images(images, format):\n",
    "    \"\"\"Visualize images in a grid of 4 rows x 4 columns.\"\"\"\n",
    "    # Create a list of 16 images by random sampling from the dataset first dimension without replacement\n",
    "    image_list = [images[i] for i in np.random.choice(images.shape[0], 16, replace=False)]\n",
    "    if format=='channels_first':\n",
    "        image_list = [np.transpose(image, (1, 2, 0)) for image in image_list]\n",
    "    \n",
    "    # check if the images are normalized\n",
    "    norm = np.max(image_list) <= 1.0\n",
    "\n",
    "    # Create a 4x4 grid of images\n",
    "    fig, axes = plt.subplots(nrows=4, ncols=4, figsize=(10, 10))\n",
    "    for i, ax in enumerate(axes.flat): # type: ignore\n",
    "        if norm:\n",
    "            ax.imshow(image_list[i]) # no need to convert to uint8\n",
    "        else:\n",
    "            ax.imshow(image_list[i].astype(np.uint8)) # convert to uint8 for visualization\n",
    "        ax.axis('off')\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def image_comparison(img, reco, format):\n",
    "    \"\"\"Visualize the original image and the reconstructed image side by side.\"\"\"\n",
    "    if format=='channels_first':\n",
    "        img = np.transpose(img, (1, 2, 0))\n",
    "        reco = np.transpose(reco, (1, 2, 0))\n",
    "    \n",
    "    # check if the images are normalized\n",
    "    norm_img  = np.max(img)  <= 1.0\n",
    "    norm_reco = np.max(reco) <= 1.0\n",
    "\n",
    "    # Create a 4x4 grid of images\n",
    "    fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 10))\n",
    "    axes[0].imshow(img if norm_img else img.astype(np.uint8)) # convert to uint8 for visualization\n",
    "    axes[1].imshow(reco if norm_reco else reco.astype(np.uint8)) # convert to uint8 for visualization\n",
    "    \n",
    "    axes[0].axis('off')\n",
    "    axes[1].axis('off')\n",
    "\n",
    "    # Show the plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_reco, rate_reco = vae(img, training=False)\n",
    "\n",
    "img_reco_2, rate_reco_2i, rate_reco_2b = vae_2(img, training=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#img      = np.squeeze(img, axis=0)\n",
    "img_reco = tf.squeeze(img_reco, axis=0).numpy()\n",
    "\n",
    "img_reco_2 = tf.squeeze(img_reco_2, axis=0).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image_comparison(np.squeeze(img, axis=0), img_reco, CH_FORMAT)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coding efficiency FFP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Compressor(tf.keras.Model):\n",
    "\n",
    "    def __init__(self, model):\n",
    "\n",
    "        super(Compressor, self).__init__()\n",
    "\n",
    "        self.encoder = model.encoder\n",
    "        self.prior   = model.prior\n",
    "        self.bemodel = tfc.ContinuousBatchedEntropyModel(\n",
    "            prior = self.prior,\n",
    "            coding_rank=1,\n",
    "            compression=True\n",
    "        )\n",
    "\n",
    "    def call(self, inputs):\n",
    "\n",
    "        encoded = self.encoder(inputs, training=False)\n",
    "\n",
    "        _, bits   = self.bemodel(encoded, training=False)\n",
    "        bitstring = self.bemodel.compress(encoded)\n",
    "\n",
    "        return bitstring, bits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "compressor = Compressor(vae)\n",
    "\n",
    "bitstrings, rates = compressor(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'92b516e922d71a958d6749ba61ede96a1ee6f9268da527d1f090cc29ff966e03722f9c94af17a231d5b80358a4365f339934edf1cd7bc63c7de773be5abee4e7c4ac17413d0f1b71aa9d000d5fe0bbfaebb0643fc1be9ba48dfc7d144071f6568ae5988f2234a923c8cdfd05cd8ae1c59a172f22f6220afbc9a8b9cc175723d7c5'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bitstrings[0, 0, 0].numpy().hex()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1030.5355"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.reduce_mean(rates).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "`set_weights` expects a list of 11 arrays, received 8.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[45], line 8\u001b[0m\n\u001b[1;32m      1\u001b[0m bemodel \u001b[39m=\u001b[39m tfc\u001b[39m.\u001b[39mContinuousBatchedEntropyModel(\n\u001b[1;32m      2\u001b[0m     prior\u001b[39m=\u001b[39mtfc\u001b[39m.\u001b[39mdistributions\u001b[39m.\u001b[39mNoisyDeepFactorized(),\n\u001b[1;32m      3\u001b[0m     coding_rank\u001b[39m=\u001b[39m\u001b[39m3\u001b[39m,\n\u001b[1;32m      4\u001b[0m     compression\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m\n\u001b[1;32m      5\u001b[0m )\n\u001b[1;32m      7\u001b[0m bemodel\u001b[39m.\u001b[39mget_config()\n\u001b[0;32m----> 8\u001b[0m bemodel\u001b[39m.\u001b[39;49mset_weights(vae\u001b[39m.\u001b[39;49mbemodel\u001b[39m.\u001b[39;49mget_weights())\n",
      "File \u001b[0;32m~/data/miniconda3/envs/tf2/lib/python3.9/site-packages/tensorflow_compression/python/entropy_models/continuous_base.py:367\u001b[0m, in \u001b[0;36mContinuousEntropyModelBase.set_weights\u001b[0;34m(self, weights)\u001b[0m\n\u001b[1;32m    365\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mset_weights\u001b[39m(\u001b[39mself\u001b[39m, weights):\n\u001b[1;32m    366\u001b[0m   \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(weights) \u001b[39m!=\u001b[39m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvariables):\n\u001b[0;32m--> 367\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    368\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m`set_weights` expects a list of \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m arrays, received \u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    369\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(\u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvariables), \u001b[39mlen\u001b[39m(weights)))\n\u001b[1;32m    370\u001b[0m   tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mbackend\u001b[39m.\u001b[39mbatch_set_value(\u001b[39mzip\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mvariables, weights))\n",
      "\u001b[0;31mValueError\u001b[0m: `set_weights` expects a list of 11 arrays, received 8."
     ]
    }
   ],
   "source": [
    "bemodel = tfc.ContinuousBatchedEntropyModel(\n",
    "    prior=tfc.distributions.NoisyDeepFactorized(),\n",
    "    coding_rank=3,\n",
    "    compression=True\n",
    ")\n",
    "\n",
    "bemodel.get_config()\n",
    "bemodel.set_weights(vae.bemodel.get_weights())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(bemodel.compress(encoded_img)[0, 2, 6].numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], dtype=float32)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_reco_2[img_reco_2 <= 0.]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TensorShape([1, 192, 19, 19])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "For range coding, the entropy model must be instantiated with `compression=True`.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)\n",
      "Cell \u001b[0;32mIn[46], line 1\u001b[0m\n",
      "\u001b[0;32m----> 1\u001b[0m vae\u001b[39m.\u001b[39;49mbemodel\u001b[39m.\u001b[39;49mcompress(encoded_img)\n",
      "\n",
      "File \u001b[0;32m~/data/miniconda3/envs/tf2/lib/python3.9/site-packages/tensorflow/python/module/module.py:311\u001b[0m, in \u001b[0;36mModule.with_name_scope.<locals>.method_with_name_scope\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m    309\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mmethod_with_name_scope\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n",
      "\u001b[1;32m    310\u001b[0m   \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname_scope:\n",
      "\u001b[0;32m--> 311\u001b[0m     \u001b[39mreturn\u001b[39;00m method(\u001b[39mself\u001b[39;49m, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "\n",
      "File \u001b[0;32m~/data/miniconda3/envs/tf2/lib/python3.9/site-packages/tensorflow_compression/python/entropy_models/continuous_batched.py:380\u001b[0m, in \u001b[0;36mContinuousBatchedEntropyModel.compress\u001b[0;34m(self, bottleneck)\u001b[0m\n",
      "\u001b[1;32m    378\u001b[0m symbols \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mcast(tf\u001b[39m.\u001b[39mround(bottleneck), tf\u001b[39m.\u001b[39mint32)\n",
      "\u001b[1;32m    379\u001b[0m symbols \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mreshape(symbols, tf\u001b[39m.\u001b[39mconcat([iid_shape, [\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]], \u001b[39m0\u001b[39m))\n",
      "\u001b[0;32m--> 380\u001b[0m symbols \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcdf_offset\n",
      "\u001b[1;32m    381\u001b[0m handle \u001b[39m=\u001b[39m gen_ops\u001b[39m.\u001b[39mcreate_range_encoder(batch_shape, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcdf)\n",
      "\u001b[1;32m    382\u001b[0m handle \u001b[39m=\u001b[39m gen_ops\u001b[39m.\u001b[39mentropy_encode_channel(handle, symbols)\n",
      "\n",
      "File \u001b[0;32m~/data/miniconda3/envs/tf2/lib/python3.9/site-packages/tensorflow_compression/python/entropy_models/continuous_base.py:124\u001b[0m, in \u001b[0;36mContinuousEntropyModelBase.cdf_offset\u001b[0;34m(self)\u001b[0m\n",
      "\u001b[1;32m    121\u001b[0m \u001b[39m@property\u001b[39m\n",
      "\u001b[1;32m    122\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcdf_offset\u001b[39m(\u001b[39mself\u001b[39m):\n",
      "\u001b[1;32m    123\u001b[0m \u001b[39m  \u001b[39m\u001b[39m\"\"\"The CDF offsets used by range coding.\"\"\"\u001b[39;00m\n",
      "\u001b[0;32m--> 124\u001b[0m   \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_check_compression()\n",
      "\u001b[1;32m    125\u001b[0m   \u001b[39mreturn\u001b[39;00m tf\u001b[39m.\u001b[39mconvert_to_tensor(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_cdf_offset)\n",
      "\n",
      "File \u001b[0;32m~/data/miniconda3/envs/tf2/lib/python3.9/site-packages/tensorflow_compression/python/entropy_models/continuous_base.py:96\u001b[0m, in \u001b[0;36mContinuousEntropyModelBase._check_compression\u001b[0;34m(self)\u001b[0m\n",
      "\u001b[1;32m     94\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_check_compression\u001b[39m(\u001b[39mself\u001b[39m):\n",
      "\u001b[1;32m     95\u001b[0m   \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcompression:\n",
      "\u001b[0;32m---> 96\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mRuntimeError\u001b[39;00m(\n",
      "\u001b[1;32m     97\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mFor range coding, the entropy model must be instantiated with \u001b[39m\u001b[39m\"\u001b[39m\n",
      "\u001b[1;32m     98\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39m`compression=True`.\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\n",
      "\u001b[0;31mRuntimeError\u001b[0m: For range coding, the entropy model must be instantiated with `compression=True`."
     ]
    }
   ],
   "source": [
    "vae.bemodel.compress(encoded_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([[[0.3477062 ],\n",
       "         [0.06208622],\n",
       "         [0.33013475]]], dtype=float32),\n",
       " array([[[-0.3699839 ],\n",
       "         [ 0.39880884],\n",
       "         [ 0.41399515]]], dtype=float32),\n",
       " array([[[-0.4717313]]], dtype=float32),\n",
       " array([[[0.],\n",
       "         [0.],\n",
       "         [0.]]], dtype=float32),\n",
       " array([[[0.],\n",
       "         [0.],\n",
       "         [0.]]], dtype=float32),\n",
       " array([[[-1.7877836],\n",
       "         [-1.7877836],\n",
       "         [-1.7877836]]], dtype=float32),\n",
       " array([[[-1.7877836, -1.7877836, -1.7877836],\n",
       "         [-1.7877836, -1.7877836, -1.7877836],\n",
       "         [-1.7877836, -1.7877836, -1.7877836]]], dtype=float32),\n",
       " array([[[-0.5264881, -0.5264881, -0.5264881]]], dtype=float32)]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vae.bemodel.get_weights()"
   ]
  }
 ],
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